Person search aims at identifying a query person from natural, uncropped images across different views [1]}, [2]}. To tackle this task, we need not only address the challenges in person re-identification (re-ID) (e.g., pose variations, occlusions, and varied camera views), but also unify pedestrian detection and person re-ID into a joint framework.
By simultaneously solving the two sub-tasks, person search exhibits its unique advantages in terms of efficiency, accuracy, and practicability.
In the past few years, rapid progresses in person search have been made with the renaissance of deep learning techniques.
Existing person search approaches can be generally divided into two categories, i.e., two-step approaches [3]}, [4]}, [5]}, [6]} and one-step ones [1]}, [8]}, [9]}. In two-step approaches, person search is decomposed into two sub-tasks, i.e., pedestrian detection and person re-ID. Detection and re-ID models typically work in a sequential manner, i.e., the detection model is used to crop the person regions, which are subsequently fed to the re-ID model to extract discriminative embeddings for identification.
However, sequentially dealing with the two tasks with two separate networks is both time- and resource-consuming. In contrast, one-step approaches [1]}, [11]}, [12]}, [9]} integrate detection and re-ID into a unified end-to-end network. Instead of explicitly employing the cropped person regions from the detection results, one-step models apply an RoI-Align layer to aggregate the features within the detected bounding boxes.
